docs/CustomizingPipeline.ipynb

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    "## Customizing the pipeline\n",
    "\n",
    "### Reading the corpus\n",
    "\n",
    "The Corpus class that represents the feature matrix which is the starting point for the analysis basically is a data frame that maps combinations of documents and features to feature counts. When _building_ the representation, it uses two helper classes to decouple aspects that are relevant for building the feature matrix: FeatureGenerator performs the actual reading and feature counting, DocumentDescriber provides labels and groupings for the documents that can be used in visualization and evaluation steps.\n",
    "\n",
    "When you just create a corpus using `delta.Corpus(directory)`, default versions will be used, but stuff can be customized:"
   ]
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     "text": [
      "Help on function __init__ in module delta.corpus:\n",
      "\n",
      "__init__(self, source=None, /, *, subdir=None, file=None, corpus=None, feature_generator=None, document_describer=<delta.util.DefaultDocumentDescriber object at 0x7f9fd139d070>, metadata=None, **kwargs)\n",
      "    Creates a new Corpus.\n",
      "    \n",
      "    You can create a corpus either from a filesystem subdir with raw text files, or from a CSV file with\n",
      "    a document-term matrix, or from another corpus or dataframe that contains (potentially preprocessed)\n",
      "    document/term vectors. Either option may be passed via appropriately named keyword argument or as\n",
      "    the only positional argument, but exactly one must be present.\n",
      "    \n",
      "    If you pass a subdirectory, Corpus will call a `FeatureGenerator` to read and parse the files and to\n",
      "    generate a default word count. The default implementation will search for plain text files ``*.txt``\n",
      "    inside the directory and parse them using a simple regular expression. It has a few options, e.g.,\n",
      "    ``glob`` and ``lower_case``, that can also be passed directly to corpus as keyword arguments. E.g.,\n",
      "    ``Corpus('texts', glob='plain-*.txt', lower_case=True)`` will look for files called plain-xxx.txt and\n",
      "    convert it to lower case before tokenizing. See `FeatureGenerator` for more details.\n",
      "    \n",
      "    The ``document_describer`` can contain per-document metadata which can be used, e.g, as ground truth.\n",
      "    \n",
      "    The ``metadata`` record contains global metadata (e.g., which transformations have already been performed),\n",
      "    they will be inherited from a ``corpus`` argument, all additional keyword arguments will be included\n",
      "    with this record.\n",
      "    \n",
      "    Args:\n",
      "        source: Positional variant of either subdir, file, or corpus\n",
      "        subdir (str): Path to a subdirectory containing the (unprocessed) corpus data.\n",
      "        file (str): Path to a CSV file containing the feature vectors.\n",
      "        corpus (pandas.DataFrame): A dataframe or :class:`Corpus` from which to create a new corpus, as a copy.\n",
      "        feature_generator (FeatureGenerator): A customizeable helper class that will process a `subdir` to a feature matrix, if the `subdir` argument is also given. If None, a default feature generator will be used.\n",
      "        metadata (dict): A dictionary with metadata to copy into the new corpus.\n",
      "        **kwargs: Additionally, if feature_generator is None and subdir is not None, you can pass FeatureGenerator\n",
      "            arguments and they will be used when instantiating the feature generator\n",
      "            Additional keyword arguments will be set in the metadata record of the new corpus.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import delta\n",
    "from matplotlib import pyplot as plt\n",
    "help(delta.Corpus.__init__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E.g., to create a corpus that ignores case differences and reads only the first 5000 tokens of each text (`lower_case` and `max_tokens` are arguments to the default FeatureGenerator):"
   ]
  },
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       "      <th>ward_milly</th>\n",
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       "                       say  sir   how   its  house   way  after  father  long  \\\n",
       "ward_ashe              6.0  2.0  10.0   4.0    8.0   5.0    6.0     4.0   3.0   \n",
       "blackmore_springhaven  2.0  1.0   7.0  12.0    3.0   7.0    5.0     5.0   9.0   \n",
       "stevenson_island       5.0  4.0   4.0   0.0    7.0  10.0    5.0    12.0   5.0   \n",
       "thackeray_esmond       8.0  3.0  10.0   3.0    9.0   2.0   17.0    14.0   5.0   \n",
       "ward_milly             4.0  1.0  14.0   5.0    8.0  11.0    5.0    15.0  12.0   \n",
       "\n",
       "                       mother  \n",
       "ward_ashe                17.0  \n",
       "blackmore_springhaven     2.0  \n",
       "stevenson_island          5.0  \n",
       "thackeray_esmond         13.0  \n",
       "ward_milly               48.0  "
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   ],
   "source": [
    "corpus = delta.Corpus('../../refcor/English', lower_case=True, max_tokens=5000)\n",
    "corpus.iloc[:5,100:110]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to roll your own feature generator. As an example, we would like to use lemmas instead of word forms as features. For that, we would like to read a version of our corpus that has been preprocessed using the DARIAH DKPro wrapper.\n",
    "\n",
    "A file like this is essentially a table that contains a line per token with various analyses in the columns:"
   ]
  },
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       "      SectionId  ParagraphId  SentenceId  TokenId  Begin    End       Token  \\\n",
       "1003          _            8          24     1003   5826   5836  Thatsachen   \n",
       "1004          _            8          24     1004   5837   5844     annimmt   \n",
       "1005          _            8          24     1005   5844   5845           ,   \n",
       "1006          _            8          24     1006   5846   5848          so   \n",
       "1007          _            8          24     1007   5849   5853        wird   \n",
       "...         ...          ...         ...      ...    ...    ...         ...   \n",
       "10005         _           26         312    10005  55649  55650           ;   \n",
       "10006         _           26         312    10006  55651  55654         sie   \n",
       "10007         _           26         312    10007  55655  55660       sagte   \n",
       "10008         _           26         312    10008  55661  55664         ihm   \n",
       "10009         _           26         312    10009  55665  55669        kein   \n",
       "\n",
       "            Lemma  CPOS    POS  ... Morphology   Hyphenation DependencyHead  \\\n",
       "1003   Thatsachen    NN     NN  ...          _  That-sa-chen           1002   \n",
       "1004     annehmen     V  VVFIN  ...          _      an-nimmt            991   \n",
       "1005            ,  PUNC     $,  ...          _             ,           1004   \n",
       "1006           so   ADV    ADV  ...          _            so           1007   \n",
       "1007       werden     V  VAFIN  ...          _          wird             -1   \n",
       "...           ...   ...    ...  ...        ...           ...            ...   \n",
       "10005           ;  PUNC     $.  ...          _             ;          10004   \n",
       "10006         sie    PR   PPER  ...          _           sie          10007   \n",
       "10007       sagen     V  VVFIN  ...          _        sag-te           9997   \n",
       "10008          er    PR   PPER  ...          _           ihm          10007   \n",
       "10009       keine    PR   PIAT  ...          _          kein          10010   \n",
       "\n",
       "       DependencyRelation NamedEntity QuoteMarker  CoreferenceChainIds  \\\n",
       "1003                   NK           _           0                    _   \n",
       "1004                   CJ           _           0                    _   \n",
       "1005                   --           _           0                    _   \n",
       "1006                   MO           _           0                    _   \n",
       "1007                   --           _           0                    _   \n",
       "...                   ...         ...         ...                  ...   \n",
       "10005                  --           _           0                    _   \n",
       "10006                  SB           _           0                    _   \n",
       "10007                  CJ           _           0                    _   \n",
       "10008                  DA           _           0                    _   \n",
       "10009                  NK           _           0                    _   \n",
       "\n",
       "      SyntaxTree Predicate SemanticArgumentIndex  \n",
       "1003           _         _                     _  \n",
       "1004           _         _                     _  \n",
       "1005           _         _                     _  \n",
       "1006           _         _                     _  \n",
       "1007           _         _                     _  \n",
       "...          ...       ...                   ...  \n",
       "10005          _         _                     _  \n",
       "10006          _         _                     _  \n",
       "10007          _         _                     _  \n",
       "10008          _         _                     _  \n",
       "10009          _         _                     _  \n",
       "\n",
       "[9007 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "dof_example = pd.read_table('../../refcor/dof/German/Huber,-Therese_Luise.txt.csv', quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "dof_example.iloc[1003:10010]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, here is a simple lemma-based feature generator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DOFLemmaFG(delta.FeatureGenerator):\n",
    "    \n",
    "    def process_file(self, filename):\n",
    "        dof = pd.read_table(filename, sep='\\t', quoting=csv.QUOTE_NONE)\n",
    "        tokens = dof[dof.CPOS != 'PUNC']                  # only non-punctuation tokens\n",
    "        counts = tokens.Lemma.value_counts()              # count the different values -> Series\n",
    "        counts.name = self.get_name(filename)\n",
    "        return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can pass this in to Corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lieb</th>\n",
       "      <th>glauben</th>\n",
       "      <th>Mutter</th>\n",
       "      <th>ob</th>\n",
       "      <th>beide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goethe,-Johann-Wolfgang_Die%20Wahlverwandtschaften.txt</th>\n",
       "      <td>64.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raabe,-Wilhelm_Im%20alten%20Eisen.txt</th>\n",
       "      <td>121.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arnim,-Ludwig-Achim-von_Armut%20Reichtum%20Schuld%20und%20Buße%20der%20Gräfin%20Dolores.txt</th>\n",
       "      <td>152.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fischer,-Caroline-Auguste_Die%20Honigmonathe.txt</th>\n",
       "      <td>23.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ebner-Eschenbach,-Marie-von_Bozena.txt</th>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     lieb  glauben  Mutter  \\\n",
       "Goethe,-Johann-Wolfgang_Die%20Wahlverwandtschaf...   64.0     69.0    24.0   \n",
       "Raabe,-Wilhelm_Im%20alten%20Eisen.txt               121.0     21.0   149.0   \n",
       "Arnim,-Ludwig-Achim-von_Armut%20Reichtum%20Schu...  152.0    124.0    95.0   \n",
       "Fischer,-Caroline-Auguste_Die%20Honigmonathe.txt     23.0     78.0    52.0   \n",
       "Ebner-Eschenbach,-Marie-von_Bozena.txt               39.0     33.0    74.0   \n",
       "\n",
       "                                                       ob  beide  \n",
       "Goethe,-Johann-Wolfgang_Die%20Wahlverwandtschaf...   55.0   93.0  \n",
       "Raabe,-Wilhelm_Im%20alten%20Eisen.txt                40.0   71.0  \n",
       "Arnim,-Ludwig-Achim-von_Armut%20Reichtum%20Schu...  107.0  188.0  \n",
       "Fischer,-Caroline-Auguste_Die%20Honigmonathe.txt     36.0    3.0  \n",
       "Ebner-Eschenbach,-Marie-von_Bozena.txt               44.0   57.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemma_corpus = delta.Corpus('../../refcor/dof/German', feature_generator=DOFLemmaFG(glob='*.txt.csv'), parallel=True)\n",
    "lemma_corpus.iloc[0:5,125:130]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rest of the steps can be performed as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tv/git/pydelta/delta/graphics.py:88: MatplotlibDeprecationWarning: Passing the pad parameter of tight_layout() positionally is deprecated since Matplotlib 3.3; the parameter will become keyword-only two minor releases later.\n",
      "  plt.tight_layout(2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c2500 = lemma_corpus.get_mfw_table(2500)\n",
    "distances = delta.functions.cosine_delta(c2500)\n",
    "clustering = delta.Clustering(distances)\n",
    "delta.Dendrogram(clustering).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F-Ratio                0.364821\n",
       "Fisher's LD            1.501815\n",
       "Simple Score           3.829410\n",
       "Cluster Errors         1.000000\n",
       "Adjusted Rand Index    0.965961\n",
       "Homogeneity            0.990683\n",
       "Completeness           0.992079\n",
       "V Measure              0.991380\n",
       "Purity                 0.986667\n",
       "Entropy                0.009317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat((distances.evaluate(), clustering.fclustering().evaluate()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deltas and Normalizations\n",
    "\n",
    "pyDelta provides a number of different delta functions, and it's easy to roll your own. You can list all available delta functions by looking at `delta.functions`. The functions are then available as, e.g., `delta.functions.burrows(corpus)`, and they all return a distance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Delta Functions</h4><ol><li><code>Linear Delta</code> = linear</li><li><code>distance</code> = linear2</li><li><code>_classic_delta</code> = burrows2</li><li><code>manhattan</code> <em>Manhattan Distance</em></li><li><code>euclidean</code> <em>Euclidean Distance</em></li><li><code>sqeuclidean</code> <em>Squared Euclidean Distance</em></li><li><code>cosine</code> <em>Cosine Distance</em></li><li><code>canberra</code> <em>Canberra Distance</em></li><li><code>braycurtis</code> <em>Bray-Curtis Distance</em></li><li><code>correlation</code> <em>Correlation Distance</em></li><li><code>chebyshev</code> <em>Chebyshev Distance</em></li><li><code>burrows</code> <em>Burrows' Delta</em> = manhattan-z_score</li><li><code>quadratic</code> <em>Quadratic Delta</em> = sqeuclidean-z_score</li><li><code>eder</code> <em>Eder's Delta</em> = manhattan-z_score-eder_std</li><li><code>eder_simple</code> <em>Eder's Simple</em> = manhattan-sqrt</li><li><code>cosine_delta</code> <em>Cosine Delta</em> = cosine-z_score</ol><h4>Normalizations</h4><ol><li>z_score (Z-Score)</li><li>eder_std</li><li>binarize</li><li>length_normalized</li><li>diversity_scaled</li><li>sqrt</li><li>clamp</li><li>ternarize"
      ],
      "text/plain": [
       "<delta.deltas._FunctionRegistry at 0x7f9fd13af250>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the functions (e.g., `euclidean`) are simply wrappers around the corresponding functions of scipy.spatial.distance. So, if you would like to provide the Minkowski norm with $p = \\frac{1}{2}$, you can simply create the function using the wrapper class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<code>l_half</code> <em>Minkowki Norm $L_½$</em>"
      ],
      "text/plain": [
       "<delta.deltas.MetricDeltaFunction at 0x7f9fbf082e20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.MetricDeltaFunction('minkowski', name='l_half', title='Minkowki Norm $L_½$', p=1/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it is possible to implement a delta function from scratch (linear delta is such a case), most “interesting” functions are a _composite_ delta function, i.e. a combination of one or more _normalizations_ and a basis distance function. E.g., Burrows' classic delta works by first normalizing the distance matrix to z-scores and then applying manhatten distance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<code>burrows</code> <em>Burrows' Delta</em> = manhattan-z_score"
      ],
      "text/plain": [
       "<delta.deltas.CompositeDeltaFunction at 0x7f9fca801c10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.functions.burrows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also roll our own. E.g., the `ternarize` normalization reduces the z-scores to -1, 0, or 1:\n",
    "$$\n",
    "t(z_{i,j}) = \\begin{cases}\n",
    "  -1, & z_{i,j} < -0.43 \\\\\n",
    "  \\ 0, & -0.43 \\le z_{i, j} < 0.43 \\\\\n",
    "  +1, & 0.43 < z_{i, j}\n",
    "\\end{cases}\n",
    "$$\n",
    "`eder_std` weights each word's influence by rank: $e(w_i) = w_i \\frac{n-i}{n}$ for $n$ words, with $w_0$ being the most frequent. We can combine this to ternarized eder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<code>eder_ternarized</code> = manhattan-z_score-ternarize-eder_std"
      ],
      "text/plain": [
       "<delta.deltas.CompositeDeltaFunction at 0x7f9fbf0827f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.CompositeDeltaFunction(name='eder_ternarized', descriptor='manhattan-z_score-ternarize-eder_std')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're able to run our analyzis on this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tv/git/pydelta/delta/graphics.py:88: MatplotlibDeprecationWarning: Passing the pad parameter of tight_layout() positionally is deprecated since Matplotlib 3.3; the parameter will become keyword-only two minor releases later.\n",
      "  plt.tight_layout(2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "et_clust = delta.Clustering(delta.functions.eder_ternarized(c2500))\n",
    "plt.figure(figsize=(10,15))\n",
    "delta.Dendrogram(et_clust).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also define your own normalizations. E.g., the original z-score normalization has been defined by these few lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registering z_score (Z-Score) as z_score, replacing existing function with this name\n"
     ]
    }
   ],
   "source": [
    "@delta.normalization(title=\"Z-Score\")\n",
    "def z_score(corpus):\n",
    "    \"\"\"Normalizes the corpus to the z-scores.\"\"\"\n",
    "    return (corpus - corpus.mean()) / corpus.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pydelta",
   "language": "python",
   "name": "pydelta"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}